{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# SVM训练与评估",
   "id": "eef3e2d388368b1e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:19:31.815643Z",
     "start_time": "2025-06-12T13:19:31.796649Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time  # 新增时间模块\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    classification_report,\n",
    "    ConfusionMatrixDisplay,\n",
    "    precision_score,\n",
    "    recall_score,\n",
    "    f1_score,\n",
    "    confusion_matrix,\n",
    ")"
   ],
   "id": "75289be7b89b49e5",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:19:34.252706Z",
     "start_time": "2025-06-12T13:19:31.819628Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 1. 加载 MNIST 数据集\n",
    "mnist = fetch_openml(\"mnist_784\", version=1, as_frame=False, parser=\"auto\")\n",
    "X, y = mnist.data, mnist.target.astype(np.uint8)\n",
    "X = X.astype(np.float32) / 255.0  # 归一化"
   ],
   "id": "f950745a5aa19959",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:19:34.738460Z",
     "start_time": "2025-06-12T13:19:34.269288Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2. 划分数据集\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ],
   "id": "b54de93c26bad776",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:23:13.309137Z",
     "start_time": "2025-06-12T13:19:34.754958Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 3. 训练模型并记录时间\n",
    "svm_model = SVC(kernel=\"rbf\", C=10, gamma=0.01)\n",
    "start_time = time.time()  # 开始计时\n",
    "svm_model.fit(X_train, y_train)\n",
    "training_time = time.time() - start_time  # 计算训练时间"
   ],
   "id": "97f9d25730249e6e",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:25:59.645551Z",
     "start_time": "2025-06-12T13:23:13.401132Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 4. 预测并记录时间\n",
    "start_test_time = time.time()\n",
    "y_pred = svm_model.predict(X_test)\n",
    "test_time = time.time() - start_test_time  # 计算预测时间"
   ],
   "id": "2f9c7f57ee855f38",
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:25:59.739084Z",
     "start_time": "2025-06-12T13:25:59.676943Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 5. 计算评估指标\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average=\"macro\")\n",
    "recall = recall_score(y_test, y_pred, average=\"macro\")\n",
    "f1 = f1_score(y_test, y_pred, average=\"macro\")\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)"
   ],
   "id": "cc0152345b2f3ef1",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:25:59.754447Z",
     "start_time": "2025-06-12T13:25:59.746068Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 输出结果\n",
    "print(f\"训练时间: {training_time:.4f} 秒\")\n",
    "print(f\"预测时间: {test_time:.4f} 秒\")\n",
    "print(f\"准确率: {accuracy:.4f}\")\n",
    "print(f\"精确率: {precision:.4f}\")\n",
    "print(f\"召回率: {recall:.4f}\")\n",
    "print(f\"F1分数: {f1:.4f}\\n\")\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)"
   ],
   "id": "151409c98ac87c09",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练时间: 218.5102 秒\n",
      "预测时间: 166.2222 秒\n",
      "准确率: 0.9810\n",
      "精确率: 0.9809\n",
      "召回率: 0.9809\n",
      "F1分数: 0.9809\n",
      "\n",
      "Confusion Matrix:\n",
      " [[1329    1    4    0    1    0    3    1    4    0]\n",
      " [   0 1588    3    4    1    0    0    4    0    0]\n",
      " [   3    3 1355    2    2    1    2    6    5    1]\n",
      " [   1    1   12 1397    2    9    0    5    3    3]\n",
      " [   2    0    2    0 1272    0    2    4    1   12]\n",
      " [   0    1    1    9    2 1247    8    0    5    0]\n",
      " [   1    0    0    0    5    4 1385    0    1    0]\n",
      " [   1    4   12    0    6    1    0 1474    1    4]\n",
      " [   3    4    6    8    1    5    5    4 1314    7]\n",
      " [   6    5    1    8    9    2    0   11    5 1373]]\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:25:59.804740Z",
     "start_time": "2025-06-12T13:25:59.771447Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 6. 详细分类报告\n",
    "print(\"分类报告:\")\n",
    "print(classification_report(y_test, y_pred))"
   ],
   "id": "7c67386ee3133550",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99      1343\n",
      "           1       0.99      0.99      0.99      1600\n",
      "           2       0.97      0.98      0.98      1380\n",
      "           3       0.98      0.97      0.98      1433\n",
      "           4       0.98      0.98      0.98      1295\n",
      "           5       0.98      0.98      0.98      1273\n",
      "           6       0.99      0.99      0.99      1396\n",
      "           7       0.98      0.98      0.98      1503\n",
      "           8       0.98      0.97      0.97      1357\n",
      "           9       0.98      0.97      0.97      1420\n",
      "\n",
      "    accuracy                           0.98     14000\n",
      "   macro avg       0.98      0.98      0.98     14000\n",
      "weighted avg       0.98      0.98      0.98     14000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:26:00.476819Z",
     "start_time": "2025-06-12T13:25:59.820393Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import seaborn as sns  # 新增导入\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)  # 计算原始混淆矩阵\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(\n",
    "    conf_matrix, \n",
    "    annot=True, \n",
    "    fmt=\"d\",  # 显示整数格式\n",
    "    cmap=\"Blues\",\n",
    "    cbar=False,\n",
    "    linewidths=0.5\n",
    ")\n",
    "plt.title(\"SVM - Confusion Matrix (Counts)\")  # 修改标题匹配模型类型\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.xticks(np.arange(10)+0.5, labels=range(10))  # 显示所有数字类别\n",
    "plt.yticks(np.arange(10)+0.5, labels=range(10), rotation=0)\n",
    "plt.show()"
   ],
   "id": "1703dedba001902d",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T13:26:01.124727Z",
     "start_time": "2025-06-12T13:26:00.492491Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 8. 可视化错误样本\n",
    "misclassified = np.where(y_pred != y_test)[0]\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i, bad_index in enumerate(misclassified[:10]):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    plt.imshow(X_test[bad_index].reshape(28, 28), cmap=\"gray\")\n",
    "    plt.title(f\"Pred: {y_pred[bad_index]}\\nTrue: {y_test[bad_index]}\")\n",
    "    plt.axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "4ba452c8d8f4fd63",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
